Real world evidence: a concept whose time has come?

The 21st Century Cures Act, passed by the US Congress late last year, marks a hugely significant moment for the development of medial devices and drugs. It’s wide ranging but I want to concentrate on one element: the use of Real World Evidence in the approval process for the new indications of drugs. It has caused some controversy but I believe, if managed effectively, it can lead to improved outcomes. I’d like to examine some of the real world data aggregation and analysis issues that drug companies now face to integrate Real World Evidence into their FDA approval process.

What is real world evidence?

The Cures Act provides a disarmingly simple definition of Real World Evidence: ‘Data regarding the usage, or potential benefits or risks, of a drug derived from sources other than randomized clinical trails’. Of course, randomized trials are the gold standard in terms of evidence for drug approval. The Act seeks not to supersede randomized trial data but to augment it when seeking approval for new indications of drugs that are already FDA approved for other clinical applications.

The challenge arises from a single word: ‘sources’. The Act does attempt to place limits on what can be considered acceptable sources and give flexibility as how data from these source can be interpreted. Even so, this provision of the Act envisages the real world data coming from:

Large simple trails and pragmatic clinical trials

Observational or patient registry studies

Retrospective database studies

Electronic Health Records

Administrative and healthcare claims

Public health surveillance and investigation

Patient-generated data from home-based and wearable devices

Patient information sharing networks and social media

Data fragmentation/inability to query data warehouses/registries keeps healthcare in early stages of analytics use

This is potentially a data avalanche. Just think about patient-generated data for a second. Today, 58% of US smartphone owners have downloaded, at least, one fitness or health app. And about two thirds of them open it every day. In addition to fitness-tracking devices and apps, drug companies are increasing utilizing other connected devices, such as glucose monitors, during clinical trials to monitor biomarkers during trials. These ever growing data sets has to be aggregated and analyzed before it becomes Real World Evidence.

The goal of the Act is to shortened the approval process and reduce the cost for new indications of drugs entering the market. But this cannot be achieved by putting the safety and efficacy of those drugs at risk.

In my opinion, the introduction of Real World Evidence can achieve the goals of the Cures Act but drugs companies have to achieve a higher level of content and information management for this to happen.

Drug safety and efficacy: the paradox of randomized clinical trials

Let’s return briefly to the idea of randomized trails as the gold standard. While I don’t in any way dispute that, I’d like to add a small but important caveat. In terms of testing the ‘chemical’ efficacy and safety of a drug, the closely controlled and monitored environment of a randomized clinical trail is scientifically sound. But patients on the trial are often told to stop taking other drugs and change their diet to ensure that results are not contaminated. The result is that the trial doesn’t reflect the way that people will take the drug in the real world.

The effectiveness of the drug and potential side effects of different drug combinations largely remain unknown until after the drug is approved and prescribed. Real World Evidence has the potential to deliver some of this subsidiary information prior to the initial approval and during the early stages of post-market adoption. This has led one commentator to suggest that Real World Evidence can address one of the FDA’s major concerns: ‘How to reliably communicate safety information to a subset of patients who are among the first to take a drug or who are in a drug registry program as part of a Risk Evaluation and Mitigation Strategy’.

In terms of the content produced within randomized trials, we again find a mine of potentially valuable content. Clinical trails typically produce hundreds of thousands of pages of data but only a fraction is shared or makes it to the final FDA submission. Figures suggest that the data from almost half of all randomized clinical trials is never published at all. When looking to gain approval for new indications, there has to be value in re-addressing the volumes of data created at its initial trails.

Real world evidence: are drug companies ready?

The idea of applying Real World Evidence within healthcare settings is not a new one. The FDA itself is using real world data to support post-market surveillance. It’s Sentinel Initiative has been running for almost ten years and uses data sources from Electronic Health Records and patient registries to monitor the safety of regulated medical products.

This shows what can be achieved but I think that there are a number of building blocks that most drug companies need to put in place to prepare for the Cures Act. As Real World Evidence is increasingly used to support the drug development life cycle, there is a need to make the identification, collection, aggregation, analysis and sharing of data more automated, transparent and secure.

Several data challenges existing. None more pressing than the need to be able to demonstrate data completeness. Drug companies need to show that the data they present is well-rounded, robust and meets the highest clinical standards. There is still a good deal of work to be done to ensure the interoperability and traceability of data across disparate systems. Drug companies need to find ways to quickly bring together data from trials and real world sources to provide a complete picture at a drug, indication, patient subset and individual patient level. The need for water-tight data provenance, governance and compliance has never been higher.

The first building block has to the introduction of a centralized Enterprise Information Management platform. The platform has to be able to bring together structured and unstructured data from the widest range of sources. There needs to be extensibility and scalability to securely connect with researchers, partners and regulators.

A global content platform is only part of the answer. Big Data analytics is also an essential element of making sense of real world data and converting it into the Real World Evidence required. There is simply so much data being created every day from sources such as clinical trials, Electronic Health Records and wearable health devices that identifying relevant data in a timely manner is a major challenge for every company – and not one that can be left in the hands of data scientists to solve. The latest generation of predictive and cognitive analytics will need to be applied to allow drug company executives and professionals to interrogate that data directly.